The massive digital impact on the industrial companies, economy and job market

We are already moving from an Outcome-based Economy (connected ecosystems, platform enabled market place, pay-per-outcome) to an Autonomous Pull Economy (continuous demand sensing, end-to-end automation, resource optimization, waste minimization and workforce transformation).

Key digital enablers

Digital transforms physical industries through connected products, systems, processes, people and artificial intelligence:

-          Cloud with its inexpensive & abundant storage enables aggregation of data streams from a large variety of sources.

-          Advanced Analytics offers descriptive and increasingly prescriptive decision support leveraging algorithms, automation, deep domain expertise such in material science, electrical engineering, etc.

-          Real-time Analytics enables real-time response to cyber physical systems.

-          Ubiquitous connectivity extends to physical products, infrastructure and all types of things.

-          Machines, devices, facilities, fleets and networks connect through intelligent sensors, software applications and controls.

-          New technical data standards and technical architecture bring together the different players across the ecosystem.

-          Data-driven decision making (DDD) – research indicates that companies, which use it considerably more competitive and profitable

The opportunities to aggregate and optimize

The learning experience of each machine can be aggregated into a single information system that accelerates learning across the entire machine portfolio and even entire network of the overall organization. Learning exponentially increases.

An aggregate view across machines, components, sub components and even materials enables optimal products, parts and other inputs delivered, at the right time to the right location, in the most efficient way.

With big data and new data compression techniques plant managers can track massive data streams of all devices continually and correlate diverse data from different devices & source to generate valuable insights for improvements formerly impossible. New visualization abilities, growing knowledge banks, etc. further improve decision making.

The cloud allows to overcome traditional information & data silos within organizations. It allows enables to bridge former boundaries among organizations, industries and locations.

Real-time diagnostics and predictive analytics will reduce maintenance costs and prevent machine breakdowns before they occur, avoid capital damage, revenue loss and accidents. Engineers can question systems on irregularities and receive intelligent response within seconds. Fleet and logistics will be optimized in real-time, improving the entire supply chain.

Everybody speaks about the Consumers, but the impact on the industrial companies is at least as big!

Industrial Internet advances will enable enhanced asset reliability by optimizing inspection, maintenance and repair processes. It will improve operational efficiencies across all operations down to the very device level.

GE has done a tremendous work on analyzing the impact of digital to the industrial sector. They estimate the potential benefit of digital (they call it Industrial internet) to the global economy worth $80 trillion by 2025, approximately one half of the entire global economy!

Just a one percent productivity increase in the commercial aviation industry, for example, would translate to $30 billion over 15 years. And this only counts fuel cost savings!

A one percent productivity gain in the global gas-fired power plant fleet could yield $66 billion savings in fuel consumption. Health care savings could amount $63 billion. World rail networks $27 billion. Similar savings and productivity gains apply to all industries…

Manufacturing becomes important again

Research proves that Services becomes the major GDP contributor and driver in developing countries. Among the leading nations it is between 72 percent (Japan) and 80 percent (USA).

However, manufacturing is important to sustain wages and living standard of the overall population. Industrial nations need to revisit this issue and re-build their manufacturing sector.

The Industrial Internet allows again to effectively compete with developing nations’ low manual costs.

Important success drivers

Security

Robust cyber security is essential to manage vulnerabilities and protect sensitive information and intellectual property as well as personally identifiable information (PII). It must cover the devices, networks and the cloud with vulnerability lifecycle management, end-to-end protection, intrusion detections/ prevention systems, firewalls, logging and network visibility, and sufficient security training for engineers, management and users.

Data needs to be encrypted on the devices as well as in the transmission of data.

Every player in the ecosystem has a role to play: Technical vendors (product design, supply chain, embedded security features), Asset Owners/ Operators (secure facilities and networks, cooperate with regulators and law enforcement), Regulators & Policymakers, Academics (train specialized people such as digital-mechanical engineers, data scientists, etc.)

Data Centers

The data is increasingly exponentially. From 2012 to 2025 the data will multiply by perhaps 40 times! The majority of data centers to process it in 2025 have yet to be built.

Job losses and new roles

Digitalization will bring an unprecedented change in the global job market. Many traditional professions and positions will be taken over by programs, robots and other emerging technologies. Automation will eliminate many people in the low skill levels. But also very educated people will be affected. (Read my blog on IBM Watson and the health care industry).

With the elimination of jobs new roles are emerging: Next generation engineers (blend traditional engineering skills with informatics & computing to serve as digital engineers), data scientists (who can blend statistics, data engineering, pattern recognition, advanced computing, uncertainty modelling, visualization) and user interface experts (industrial design of human-machine interaction, operation through gesturing and facial recognition, etc.)

The difference between Industrial revolution and the Industrial Internet

The instrumented industrial machine systems have connected with the physical & human networks and entered into a continuous cycle of communication, exchange and mutual learning.

While the industrial revolution focused on resources and physical objects, the Industrial Internet focuses on innovation, knowledge, software and intelligent systems & devices.

Network, fleet, asset and facility optimization happens through intelligent devices, systems and decision making.

Potential Problems & Challenges

The Industrial Internet promises us operational improvements across all industries worldwide. Based on the benefits of the Internet we can extrapolate the positive outcomes of the Industrial Internet:

-          Cost-deflation (similar as when companies adopted ICT equipment)

-          Labor productivity growth (1996 -2004 it generated 3.1 percent on average)

-          Average GDP growth could be 25 to 40 increase (based on a productivity increase of 1 percent)

However, all calculations are based on the assumption that labor and capital would accumulate at the same pace. That is not realistic, given that perhaps 20 to 25 percent of jobs may become obsolete by 2025. And it is simply not sensible to believe that shop keepers, manual labor workers and other less skilled people can be up-skilled enough to stay competitive. What to do with these people?

 This article incorporates thoughts, research & content of GE as well as of other thought leaders


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To share your own thoughts or other best practices about this topic, please email me directly to alexwsteinberg (@) gmail.com.

Alternatively, you also may connect with me and become part of my professional network of Business, Digital, Technology & Sustainability experts at

https://www.linkedin.com/in/alexwsteinberg   or
Xing at https://www.xing.com/profile/Alex_Steinberg   or
Google+ at  https://plus.google.com/u/0/+AlexWSteinberg/posts


 

Big Data & Analytics - the full view (upon request)

Upon request, I have put together the five parts of the previous published Big Data series and combined into one documents. This offers you to read everything in one place. Please share your thoughts and best practices with me. You always may email me directly to alexwsteinberg@gmail.com
Big Data Series - Part 1   Technical challenges
Big Data requires to learn much about data as an asset and analytics. Data is the most precious asset in an organization, the currency of the enterprise.
Companies’ data ecosystems have become complex and littered with silos. A large majority of companies is still not able to make full use of Big Data advantages.
There are many challenges with Big Data: Lack of knowledge, varying definitions & expectations, different views about data sources and use cases, ignorance about valuable data sources, technologies, etc.
Companies must understand data across the entire data supply chain and their individual stages: Identifying & leveraging data sources, importing, enhancement of data value, combination with other data, generation of insight, and taking of specific actions.
This means: companies must mobilize data across the enterprise; deeply understand, analyze and determine value of respective data; understand business use case and data patterns to determine appropriate actions.
It requires companies to commit to continuous discovery, experimentation, testing, learning, adapting and innovation.
There are many approaches, solutions and technologies presently offered in the Big Data domain and quickly evolving. Companies need to be aware of the different options and their pros & cons to combine those to an overall solution.
Continue part 2 out of 5  
Big Data Series – Part 2 - Traditional data approaches not enough anymore
Given the varying types, sources and sheer size of data today the traditional approach of collecting data in a staging area, transforming into desired format, loading in mainframe/ data ware house and then delivering requested data to users on a point by point query does not work well any more.
Companies must perform calculations, run simulations models, compare statistics at fast speed to generate insights. Real-time analytical tools able to pre-process streaming data and correlate data from internal and external sources, offer interesting opportunities, but also complex challenges.
Data acceleration enables massive amounts of data to be ingested, processed, stored, queried and accessed much faster. It ensures multiple ways for data to come into the company’s data infrastructure and be referenced fast.
Data acceleration leverages hardware and software power through clustering and helps correlate different data sources, including localization. It improves interactivity by enabling users and applications to connect to the data infrastructure in universally accepted ways and ensuring that user queries are delivered as quickly as required.
Continue part 3 out of 5
Big Data Series – Part 3 Six technology components for Data Acceleration
There are at least six key technology components to build a supporting architecture: Big Data platforms, Ingestion solutions, Complex event processing, In-memory databases, Cache clusters and Appliances. Each component helps with data movement (from source to where needed), processing and interactivity (the usability of the data infrastructure).
Big Data platform (BDP)
BDP is a distributed file system and compute engine. It contains a big data core, a computer cluster with distributed data storage and computing power. Replication and sharding partitions very large databases into smaller, more easily to manage parts in order to accelerate data storage.
Newer additions enable more powerful use of core memory as a high-speed data store. These improvements allow for in-memory computing. Streaming technologies added to the core can enable real-time complex event processing. In-memory analytics support better data interactivity.
Further enhancements to the big data core create fast and familiar interfaces with data on the cluster. The core stores structured and unstructured data, but requires map/reduce functionality to read. Query engine software enables the creation of structured data tables in the core and common query functionality (SQL etc.)
Ingestion
Collecting, capturing and moving data from its sources to underlying repositories used to be done traditionally through the extract, transform and load ETL method. Today the priority is not the structure of the data as it enters the system, but assuring that all data is gathered covering different increasing data types & sources and quickly transported to areas where it can be processed by users. Ingestion solutions cover both static and real-time data. The data the gathered by the publisher and then send to a buffer/ queue, where the user can request the data.
Complex Event Processing (CEP)
After data ingestion the CEP is responsible for preprocessing and aggregation (& triggering events). It tracks, analyzes and processes data of events and derives conclusions. CEP derives data from multiple sources and combines historic as well as fresh data in order to infer patterns and to understand complex circumstances. Its engines pre-process fresh data streams from its sources, expedite processing of future data batches, match data against pre-determined patterns and trigger events based on detected patterns.
CEP offers immediate insight and enables fast action taking. In-memory computation allows to run Data movement and processing in parallel, increasing speed. CEP solutions add computing power by processing the data before it is submitted to the data stores or file systems.
In-memory databases (IMDB)
IMDBs are faster than traditional databases, because they use simpler, internal algorithms and executive fewer central processing unit instructions. The database is preloaded from disk to memory. Accessing data in memory eliminates the seek-time involved in querying data on disk storage. The applications communicate through SQL, which receives records in the RAM and triggers the query optimizer.
IMDBs constrain the entire database to a single address space. Any data can be accessed within microseconds. The steadily falling RAM prices favor this solution.
Cache Clusters
They are clusters of servers in which memory is managed by a central software designed to transfer the load from upstream data sources (databases) to applications and users. They are typically maintained in-memory and can offer fast access to frequently accessed data. They sit between the data source and the user. Traditionally they accommodate simple operations such as reading and writing values. They are populated when a query is sent from a data user to the source. Prepopulating data into a cache cluster of frequently accessed data improves response time. Data grids can take caching a step forward by supporting more complex queries and using massive parallel processing (MPP) computations.
Appliance
Massive parallel processing sits between data access and data storage. Appliance here is a pre-configured set of hardware and software including servers, memory, storage, input/output channels, operating systems, DBMS, admin software and support services.
It may have a common database for online transactions and analytical processing, which improves the interactivity and speed. Appliances can perform complex processing on massive amounts of data.
Implementing and maintaining high performance data bases on clusters is challenging and few companies have the necessary expertise to do so themselves.
Custom-silicon circuit boards enable to develop their specific solutions. It enables development on devices for specific use cases and allows for network optimization (integrating embedded logic, memory, networking and process cores). This plug and play functionality offers interesting possibilities.
Continue part 4 out of 5
Big Data Series – Part 4 Creating a suitable Technology Stack/ Solution
All of these components bring their individual technology features. Companies must wisely put together an overall solution from among those components, leveraging their complementary advantages and customizing those to their particular needs.
There are four fundamental technology stacks (with their variations) offer possible solutions:

1. Big data core only or with enhancements (with complex event processing, with in-memory database, with query engine or with complex event processing and query engine)
   • This technology is the de-facto standard for exceptional data movement, processing and interactivity.
   • Data usually enters the cluster through batch or streaming.
   • Events are not processed immediately, but in intervals. Enables parallel processing on large data sets, and thus advanced analytics.
   • Applications and services may access the core directly and deliver improved performance of large, unstructured data sets.
   • Adding CEP enhances big data core processing capabilities, real-time detection of patterns in data and trigger events. Enables real-time animated dashboards. Could add machine learning program to the CEP.
   • IMDB can further increase computing power through placing key data in RAM.
   • Query engines can further open interfaces for applications to access big data even faster.
2. In-memory data base (IMDB) cluster only or with enhancements (with Big Data Platform, with complex event processing)
   • External data is streamed in or transferred as bulk to the IMDB
   • Users and applications can directly query the IMDB, usually through SQL like structures.
   • The incoming data is first pre-processed through the BDP before it goes to the IMDB
   • In case of CEP, the CEP first ingests the data; the processing is then done in the IMDB and then returned to the application for faster interactivity.
3. Distributed Cache only or with enhancement (with Application and Big Data platform)
   • A simple caching stack sitting atop of the data source repository. The application retrieves the data. The most relevant data subset is placed in the cache.
   • Processing of the data falls to the application (may result in slower processing speeds)
   • If BDP, the BDP ingests the data from the source and does the bulk of the processing, then puts data subset in cache.
4. Appliance only or with enhancement (with Big Data platform)
   • Data streams directly into the appliances; the application talks directly to the appliance
   • If BDP, the BDP ingests and processes data. The application can directly talk to the appliance for queries.

Continue part 5 out of 5

Big Data Series – Part 5 – 12 Immediate suggestions to build a data supply chain

• Consider data as perhaps the most important asset in your organization. Become data driven. Some people call it “data religious”.
• Research about Big data & Analytics best practices. It requires continuous learning. Refer to the different approaches offered in previous blogs (Data Acceleration Part 1 and 2).
• Do an inventory of existing data. Focus on most frequently accessed and time-relevant data.
• Identify, simplify and optimize inefficient data processes. Eliminate manual, time-consuming data curation processes (such as tagging and cleaning).
• Identify currently unmet business needs and develop solutions.
• Identify and overcome data silos.
• Simplify and standardize data access through a robust data platform
• Build an effective technology stack using one of the four suggested options while leveraging some of the described six components (Data Acceleration Part 1 and 2).
• Further explore API management, traditional middleware, PaaS and other possibilities
• Analyze current internal data sources and look for still hidden sources. Explore external sources to increase quantity and quality of available data.
• Identify and improve individual data supply chain streams
• Develop a systematic roadmap for building an effective overall data supply chain

Special thanks to Accenture Technology Labs and Analytics Group, whose thought leadership, best practices and white papers have served as inspiration and knowledge source for this Big Data series.

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To share your own thoughts or other best practices about this topic, please email me directly to alexwsteinberg (@) gmail.com.

Alternatively, you also may connect with me and become part of my professional network of Business, Digital, Technology & Sustainability experts at

https://www.linkedin.com/in/alexwsteinberg   or
Xing at https://www.xing.com/profile/Alex_Steinberg   or
Google+ at  https://plus.google.com/u/0/+AlexWSteinberg/posts

Digital technology advances - How it impacts companies and the way we live and work

The digitalization of business & society and technological advancement in recent years is breath taking and has major impact on companies and each of us individuals.

Here a list of key trends & abilities that leading companies are already implementing:

Physical and digital sensors supported by high capacity networks create large, ubiquitous data streams.

In analytics powerful modelling techniques search for patterns in the data – crunching vast amounts per se or based on hypothesis – bring self-awareness to seemingly isolated activities and enable a constant cycle of feedback, learning and improvement. It helps analyze trade-offs, offer decision alternatives, empower independent machine-to-machine communication,

Software translates physical objects into data files, which then can drive programmable tools such as robotics and 3 D printing.

In cognitive augmentation technologies learn by observation and off load routine knowledge work to automated assistants. Voice & gesture recognition, natural processing language will blur separation between human and robotic team mate.

In physical augmentation automatic robotics can sense, interact with their environments and insert themselves into human work flows; a hybrid human-robot work flow results.  

In collaborative augmentation software directly improves the ways that employees work and co-create new products through social networking, real time video conferencing, virtual task and project management, crowdsourcing, discussion forums, online file sharing,

In real time adaptation digital assistants provide decision support where humans lack information or understanding and thus achieving higher productivity and operational flexibility.

In concert with intelligent machines, humans will be able to project themselves through remotely controlled avatars.

Visualization allows operators to see systems. The same tools that enliven social networking sites are migrating to the factory floor and office environment enabling communication and real-time work flow adjustments.

Work processes are increasingly structured around quick design-build-test cycles allowing for fast, cost efficient, experiment-driven design, based on digital modeling and simulation.

All comes to together

All mentioned above technologies & methods are being already implemented. A comprehensive approach can help to systematically integrate those. For example, Ubiquitous data streams and Analytics & Modelling feed Rich Digital Data representations, which then empowers, cognitive, physical and collaborative augmentation and other techniques.

(For frameworks, please refer to my other blogs)

Impact on organizations changes in management and employee roles

Becoming a digital company requires change and transformation in operational model, skills, roles, culture, and management practices. Using latest technologies is not enough to become truly digital.

Key changes are already taking place on an organizational, management and employee level:

-          Impact on organizations

o   Collaboration across remote locations, time zones and work/life space changes our office set up and employment arrangements.

o   Decision making is pushed to the most customer facing people. In edge centric decision employees make decisions locally based on rich data streams, contextual knowledge and analytics support achieving higher customer satisfaction.

o   Socio-technical approaches and Lean Design teach the need for mutual adaptation of people and technology. It is no longer the quest to find the one best way, but to create smart systems that combine the right technical solutions in the right human context.

-          Impact on Employees and Workers

o   The work force of the future will require new key skills: ability & willingness to embrace data, leverage digital tools, team with machines and technology, experiment, adopt fail quickly/ fail cheaply, continuously learn and adapt, shift from rule-following to finding solutions and adding value and exercise judgement (the key remaining difference between humans and new machine intelligence, and effectively collaborate across ecosystem).

o   Interestingly, amassing knowledge is not as important anymore. Rather, an employee must be able to quickly find, assimilate new information and make best decision relying increasingly on intelligent devices.

o   Each worker and employee can and must become much more customer focused as new digital tools manage traditional operational tasks. Engineers are already directly engaging with customers to get feedback on their design & development resulting in much shorter cycles.

o   There is a massive trend to downsize the existing work force through technology. The remaining people will need to upscale their skills. Remaining shop sales people will move from placing sales to forecasting them.

-          Impact on management

o   Managers need to create an environment where people can experiment, learn, fail and succeed.

o   Must experiment with and adapt management style based on circumstances and results.

o   Necessary skills: Coach employees through experimentation, quick failure/ learning cycle; strong numeric, communication and interpersonal skills; initiate, listen, inspire and encourage;

o   Replace dominant upfront planning with approach of re-planning and adapting to new data, insight and fast changing market conditions.

Summary:

We are at the brink of a new area, which will change the way we manage companies, work as people and life as human beings. Increasingly less people will be required in a traditional way to produce products & services. Intelligent machines and software will take their place.  

Those who want to work need to upscale their skills and smartly position themselves in roles where they can extend beyond intelligent machine limitations.


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To share your own thoughts or other best practices about this topic, please email me directly to alexwsteinberg (@) gmail.com.

Alternatively, you also may connect with me and become part of my professional network of Business, Digital, Technology & Sustainability experts at

https://www.linkedin.com/in/alexwsteinberg   or
Xing at https://www.xing.com/profile/Alex_Steinberg   or
Google+ at  https://plus.google.com/u/0/+AlexWSteinberg/posts


  

Big Data Series – Part 5 – 12 immediate suggestions to build a data supply chain

Immediate suggestions to build a respective data supply chain

-         Consider data as perhaps the most important asset in your organization. Become data driven. Some people call it “data religious”.

-          Research about Big data & Analytics best practices. It requires continuous learning. Refer to the different approaches offered in previous blogs (Data Acceleration Part 1 and 2).

-          Do an inventory of existing data. Focus on most frequently accessed and time-relevant data.

-          Identify, simplify and optimize inefficient data processes. Eliminate manual, time-consuming data curation processes (such as tagging and cleaning).

-          Identify currently unmet business needs and develop solutions.

-          Identify and overcome data silos.

-          Simplify and standardize data access through a robust data platform

-          Build an effective technology stack using one of the four suggested options while leveraging some of the described six components (Data Acceleration Part 1 and 2).

-          Further explore API management, traditional middleware, PaaS and other possibilities

-          Analyze current internal data sources and look for still hidden sources. Explore external sources to increase quantity and quality of available data.

-          Identify and improve individual data supply chain streams

-          Develop a systematic roadmap for building an effective overall data supply chain

 Special thanks to Accenture Technology Labs and Analytics Group, whose thought leadership, best practices and white papers have served as inspiration and knowledge source for this Big Data series.

Big Data Series – Part 4 Creating a suitable Technology Stack/ Solution

All of these components bring their individual technology features. Companies must wisely put together an overall solution from among those components, leveraging their complementary advantages and customizing those to their particular needs.

There are four fundamental technology stacks (with their variations) offer possible solutions:

1.       Big data core only or with enhancements (with complex event processing, with in-memory database, with query engine or with complex event processing and query engine)

o   This technology is the de-facto standard for exceptional data movement, processing and interactivity.

o   Data usually enters the cluster through batch or streaming.

o   Events are not processed immediately, but in intervals. Enables parallel processing on large data sets, and thus advanced analytics.

o   Applications and services may access the core directly and deliver improved performance of large, unstructured data sets.

o   Adding CEP enhances big data core processing capabilities, real-time detection of patterns in data and trigger events. Enables real-time animated dashboards. Could add machine learning program to the CEP.

o   IMDB can further increase computing power through placing key data in RAM.

o   Query engines can further open interfaces for applications to access big data even faster.

2.       In-memory data base (IMDB) cluster only or with enhancements (with Big Data Platform, with complex event processing)

o   External data is streamed in or transferred as bulk to the IMDB

o   Users and applications can directly query the IMDB, usually through SQL like structures.

o   The incoming data is first pre-processed through the BDP before it goes to the IMDB

o   In case of CEP, the CEP first ingests the data; the processing is then done in the IMDB and then returned to the application for faster interactivity.

3.       Distributed Cache only or with enhancement (with Application and Big Data platform)

o   A simple caching stack sitting atop of the data source repository. The application retrieves the data. The most relevant data subset is placed in the cache.

o   Processing of the data falls to the application (may result in slower processing speeds)

o   If BDP, the BDP ingests the data from the source and does the bulk of the processing, then puts data subset in cache.

4.       Appliance only or with enhancement (with Big Data platform)

o   Data streams directly into the appliances; the application talks directly to the appliance

o   If BDP, the BDP ingests and processes data. The application can directly talk to the appliance for queries.

Continue part 5 out of 5

Big Data Series – Part 2 - Traditional data approaches not enough anymore

Given the varying types, sources and sheer size of data today the traditional approach of collecting data in a staging area, transforming into desired format, loading in mainframe/ data ware house and then delivering requested data to users on a point by point query does not work well any more.

Companies must perform calculations, run simulations models, compare statistics at fast speed to generate insights. Real-time analytical tools able to pre-process streaming data and correlate data from internal and external sources, offer interesting opportunities, but also complex challenges.

Data acceleration enables massive amounts of data to be ingested, processed, stored, queried and accessed much faster. It ensures multiple ways for data to come into the company’s data infrastructure and be referenced fast.

Data acceleration leverages hardware and software power through clustering and helps correlate different data sources, including localization. It improves interactivity by enabling users and applications to connect to the data infrastructure in universally accepted ways and ensuring that user queries are delivered as quickly as required.

Continue part 3 out of 5